درخت حوزه‌های تخصصی

This study investigates the relationships between Bitcoin (BTC) prices and fluctuations in relation to gold, USD, and Iran's oil prices from 2019 to 2022. We employed the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) method to model the fluctua-tions of financial variables. Additionally, the smooth transition regression (STR) method was applied to explore the relationships between the variables. The results reveal significant positive correlations between BTC prices and gold, as well as oil, and a negative correlation with USD prices. We observed volatility persistence, causality, and phase differences between BTC and other financial instruments and indicators. Notably, a negative relationship was identified between Bitcoin and the USD in both linear and non-linear aspects, with a larger coefficient in the second regime. Furthermore, a posi-tive relationship was found between Bitcoin and the variables of gold and oil prices, with coefficients being larger in the second regime compared to the first.

The desirability of the financial reporting can greatly help the users of finan-cial information in making investment decisions. The purpose of this re-search is to measure the readability of financial reporting using a multi-indicator decision-making model and the artificial neural network method and the role of information presentation time in its improvement. In this research, various indicators have been used to measure the readability of financial reporting, and the quality of reporting is obtained through the rank-ing of companies by the stock exchange. In this research, the number of 149 companies admitted to the Tehran Stock Exchange in the period of 2010-2020 was examined, and to measure the financial readability through struc-tural equations and Stata software, and to test the hypothesis of the research, the regression model and Eviews econometrics software were used. In this study, we have tried to Use machine learning techniques and optimization tools as a way to derive adaptive-robust nonlinear models that can reduce the risk of model error as much as possible. The findings of the research show that the time of providing information has an impact on the readability of financial reporting. The obtained outputs from the estimation of the artificial neural networks and results obtained from estimation, using of this method with evaluation scales concerning random amount and comparing it with adjusted R, we found that there is meaningful relation between the associated variables and return. However, such network has the least error than other networks. The results show an overall improvement in forecasting using the neural network as compared to linear regression method. In other words, our proposed system displays an extremely higher profitability potential. The obtained result can be argued that the more the company's information is provided by the managers to the company's shareholders and investors on time and at the right time, the more readable and understandable the financial reports will be.

The increased number of restatements in recent years has increased the wor-ries about the quality of financial reporting among the beneficiary groups. The pres-ence of prior period adjustments and, subsequently, the financial restatements have a negative impact on the relatedness and reliability of the financial state-ments. The present study is aimed to present an appropriate criterion for predict-ing the financial restatements based on the Beneish model and its indices in companies admitted to the Tehran Stock & Exchange between 2009 and 2020. For this purpose, a total of 265 companies were selected considering the limitations. Also, the model estimation was per-formed using Beneish's primary model, a meta-heuristic neural network model, and optimization through genetic programming. As indicated by the obtained results based on the confusion matrix, the efficiency of the pro-posed model derived from the enhanced Beneish model with a genetic algo-rithm had a total prediction accuracy of 73.21%, which was the highest prediction power compared to the Beneish Model .

The optimal portfolio selection is vital for investment. The risk of portfolio Selection and return is the most critical concern of investment companies and private investors. According to modern portfolio theory, diversification should cover the risk. This theory is based on the normality of assets return. Experimental findings indicate that the assets return non-normality. Higher moments are sed to upgrade traditional models with the primary presumption of a normal distribution in recent years. This study uses a higher moment and the entropy for diversification and selects a portfolio given a non-normality assumption. It is essential to use up-to-date information to increase the model's efficiency, and accordingly, we used the rolling window for new price information. For the financial information method, we use the total index return in the last five working days and weigh the shares of the banking, insurance, and leasing industries on the next working day and evaluate this for three years. Python, math, and NumPy libraries were used to analyze the data. The results show that a much higher moment model can provide better portfolio selection results in most cases.

The purpose of this study was to design and formulate strategic liquidity management strategies in the banking industry. In this research, in order to combine qualitative and quantitative data, a sequential integrated exploratory method will be used, according to the classification model with emphasis on qualitative data. Therefore, according to its objectives, the present study is part of applied research and in terms of the research process is part of descriptive and exploratory research that was conducted in two parts: qualitative and quantitative. The statistical population of the present study was the qualitative part of the managers of the Welfare Bank. The sampling method was to achieve theoretical saturation and 25 people were selected as the sample size. Therefore, a survey was used to collect information and according to the data collection, two types of tools were used to review documents, interviews, and questionnaires, and the evaluation method of the questionnaire was performed with a 5-point Likert scale. The Cronbach's alpha questionnaire was used. SWOT analysis was used to analyze the data. The results showed that the Welfare Bank has many opportunities to develop appropriate liquidity management strategies. As it was observed, the chart stretches towards the opportunities and strengths of the offensive situation, which requires strategic planning to use the strengths and opportunities, and 11 strategies were developed for this purpose.

Objective: To identify the components affecting the behavior of investors and to develop a measurement model using a confirmatory factor analysis approach.Method: This is a correlational paper to identifythe dimensions and structures affecting investor behavior first using TOPSIS technique and then with first-order confirmatory factor analysis and second-order factor analysis. The statistical population includes people who have been active in the Tehran Stock Exchange for at least two years with asample of 327. The sampling method is convenience nonprobability sampling. The data was collected through a researcher-made questionnaire. The expert approval and Cronbach's alpha coefficient were used to assess the content validity. Findings: the current paperidentified seven factors as effective factors on investor behavior according to the theoretical literature and research background using TOPSIS technique. In the next stage, the research findings using the confirmatory factor analysis approach indicate that the two factors of investor financial literacy and investor personality traits have the most effective role in investor behavior. Also, the factors of higher expected returns, rules and regulations, security, profitability, position and location of investment are the next effective priorities on the behavior of investors.

The role of the capital market is fundamental and decisive in the economy of all countries [6]. Studies show that in the capital market, prices are determined based on macroeco-nomic variables[2]. Accordingly, In this study is to provide a model for measuring the impact of economic policy uncertainty (EPU) on information asymmetryn.The research method is applied and has been made to present a model for measuring the effect of EPU on information asymmetry in the EVIEWS12 and MATLAB2021 software environment. The research time period is determined from 2011 to 2020 and 101 companies are selected based on the applied restrictions to estimate the model. In this study, 40 variables affecting EPU are entered into the According to the results of BMA, the most important variables affecting the EPU is provided[2]. Based on the principal components approach, the EPU index is calculated using the most important variables affecting this variable. Then, with the GARCH model, the uncertainty part of the EPU index is extracted, and finally, using the powerful nonlinear TVPFAVAR model, the shock caused by the EPU variable on the information asymmetry indices in the research period is analyzed. The results show that the shock caused by the fluctuation of the variable of EPU has increased the index of information asymmetry in recent years. Based on the results, EPU shock on the index of information asymmetry has had a stronger effect on in the short and long term information asymmetry than the medium term.